The study aimed to investigate the associations between maternal lifestyles and antenatal stress and anxiety. 1491 pregnant women were drawn from the Guangxi birth cohort study (GBCS). A base line questionnaire was used to collect demographic information and maternal lifestyles. The Pregnancy Stress Rating Scale (PSRS) and Self-Rating Anxiety Scale (SAS) were used to assess prenatal stress and anxiety, respectively. Regression analyses identified the relationship between maternal lifestyles and prenatal stress and anxiety: (1) Hours of phone use per day was positively correlated to prenatal stress and anxiety and increased with stress and anxiety levels (all P trend < 0.05). In addition, not having baby at home was positively correlated to prenatal stress. (2) Self-reported sleep quality was negative with prenatal stress and anxiety, and decreased with stress and anxiety levels (all P trend < 0.01). Moreover, not frequent cooking was negatively correlated to prenatal stress and having pets was negatively correlated to prenatal anxiety (P < 0.05). However, having pets was not correlated to prenatal stress (P > 0.05). Our results showed that adverse lifestyles increase the risk of antenatal stress and anxiety, a regular routine and a variety of enjoyable activities decreases the risk of prenatal stress and anxiety.
Background
Observational studies present conflicting results about a possible association of iron status with asthma risk, pointing to potential modifiable targets for prevention.
Objective
The aim of this study was to use Mendelian randomization (MR) to estimate associations between iron status and asthma risk.
Methods
We used the Genetics of Iron Status consortium to identify genetic variants that could be used as instrumental variables for the effect of systemic iron status. The following sets of instruments were used: a conservative set (instruments restricted to variants with concordant relations to 4 iron status biomarkers) and a liberal set (instruments selected using variants associated with at least 1 of 4 iron status biomarkers). Associations of these genetic variants with asthma risk were estimated in data from the Trans-National Asthma Genetics Consortium (TAGC) and the GABRIEL consortium (A Multidisciplinary Study to Identify the Genetic and Environmental Causes of Asthma in the European Community). Data on the association of genetic variants with iron status and with asthma were combined to assess the influence of iron status on asthma risk.
Results
In the conservative approach, the MR OR of asthma was 1.00 (95% CI: 0.91, 1.10) per SD increase in iron, 0.96 (95% CI: 0.78, 1.18) in log-transformed ferritin, 0.99 (95% CI: 0.93, 1.06) in transferrin saturation, and 1.03 (95% CI: 0.93, 1.14) in transferrin in the TAGC dataset (none of the values were statistically significant). An age at onset–stratified analysis in the GABRIEL dataset suggested no effect of iron status in childhood onset, later onset, or unknown age at onset asthma. Findings from the liberal approach were similar, and the results persisted in sensitivity analyses (all P > 0.05).
Conclusions
This MR study does not provide evidence of an effect of iron status on asthma, suggesting that efforts to change iron concentrations will probably not result in decreased risk of asthma.
Background
Previous studies focus on one or several serum biomarkers and the risk of cardiovascular disease (CVD). This study aims to investigate the association of multiple serum biomarkers and the risk of CVD and evaluate the dose-relationship between a single serum metabolite and CVD.
Methods
Our case-control study included 161 CVD and 160 non-CVD patients who had a physical examination in the same hospital. We used stratified analysis and cubic restricted analysis to investigate the dose-response relationship of individual serum biomarkers and the CVD incident. Moreover, to investigate serum biomarkers and CVD, we used elastic net regression and logistic regression to build a multi-biomarker model.
Results
In a single serum biomarker model, we found serum FT4, T4. GLU, CREA, TG and LDL-c were positively associated with CVD. In the male group, serum T4, GLU and LDL-c were positively associated with CVD; and serum TG was positively associated with CVD in the female group. When patients ≤63 years old, serum T4, GLU, CREA and TG were positively associated with CVD, and serum TG and LDL-c were positively associated with CVD when patients > 63 years old. Moreover, serum GLU had nonlinearity relationship with CVD and serum TG and LDL-c had linearity association with CVD. Furthermore, we used elastic regression selecting 5 serum biomarkers (GLU, FT4, TG, HDL-c, LDL-c) which were independently associated with CVD incident and built multi-biomarker model. And the multi-biomarker model had much better sensitivity than single biomarker model.
Conclusion
The multi-biomarker model had much higher sensitivity than a single biomarker model for the prediction of CVD. Serum FT4, TG and LDL-c were positively associated with the risk of CVD in single and multiple serum biomarkers models, and serum TG and LDL-c had linearity relationship with CVD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.